Title: Impacts of High-Resolution Land and Ocean Surface Initialization on Local Model Predictions of Convection
1Impacts of High-Resolution Land and Ocean Surface
Initialization on Local Model Predictions of
Convection
Jonathan L. Case ENSCO, Inc./Short-term
Prediction Research and Transition (SPoRT)
Center Huntsville, Alabama
- Talk Outline
- Experiment objectives
- NASA Data and Tools
- Goddards Land Information System (LIS)
- Moderate Resolution Imaging Spectroradiometer
(MODIS) - Simulation methodology
- Preliminary results
- Future work
2Hypothesis and Experiment Objectives
- Hypothesis High-resolution land and water
datasets from NASA utilities can lead to
improvements in simulated summertimepulse-type
convection over the S.E. U.S. - Experiment objectives
- Use NASA LIS to provide high-resolution land
surface initializations - Incorporate SPoRT MODIS composites for detailed
representation of sea surface temperatures (SSTs) - Demonstrate proof of concept in using these
datasets in local model applications with the
Weather Research and Forecasting (WRF) model - Quantify possible improvements to WRF simulations
3NASA Land Information System (LIS)
- High-performance land surface modeling and data
assimilation system - Runs a variety of Land Surface Models (LSMs)
- Integrates satellite, ground, and reanalysis data
to force LSMs in offline mode - Can run coupled to Advanced Research WRF
- Data assimilation capability (EnKF) built-in
- Modular framework enables easy substitution
ofdatasets, LSMs, forcings, etc. - Adopted by AFWA for operational use in WRF
- Previous SPoRT work with LIS
- Case et al. (2008) manuscript in J. Hydrometeor.
- Quantified positive impacts to WRF forecasts over
Florida by initializing model with LIS land
surface output - Focused on verification of primary meteorological
variables
4Land Surface Modeling with LIS
Inputs
Physics
Outputs
Applications
Topography, Soils
Land Surface Models (e.g. Noah, VIC, SIB, SHEELS)
Soil Moisture Temp
Weather/ Climate Water Resources Homeland Sec
urity Military Ops Natural Hazards
Land Cover, Vegetation Properties
Evaporation
Meteorology (Atmospheric Forcing)
Runoff
Data Assimilation Modules
Snow Soil Moisture Temperature
Snowpack Properties
5MODIS SST Product
RTG
OSTIA
MODIS
Once daily 1/12 degree resolution
Once daily 5-km resolution
Four times daily 1-km resolution
- MODIS provides superior resolution
- Quality check with the latency product
- Current weakness is high latency in areas with
persistent cloud cover - Collaboration with Jet Propulsion Laboratory to
improve product with AMSR-E data
MODIS
Latency Product
6Experiment Design
- Run parallel WRF simulations
- Once daily 27-h simulations, initialized at 0300
UTC - Control Initial / boundary conditions from NCEP
12-km NAM model - Experimental Same as Control, except
- Replace land surface data with LIS output fields
- Replace SSTs with SPoRT MODIS composites
- Evaluation and Verification
- Graphical and subjective comparisons
- Verification using Meteorological Evaluation
Tools (MET) package - Developed by WRF Development Testbed Center
- Standard point/grid verification statistics
- Method for Object-Based Diagnostic Evaluation
(MODE) - Object-oriented, non-traditional verification
method - Summer convective precipitation verification
7WRF Model Configuration
- Model domain over Southeastern U.S.
- Advanced Research WRF v3.0.1.1
- 4-km horizontal grid spacing
- 39 sigma-p levels from surface to 50 mb
- Min. spacing near surface of 0.004 sigma
- Max. spacing of 0.034 sigma
- Positive definite advection of scalars
- Model physics options
- Radiation Dudhia SW and RRTM LW
- Microphysics WSM6
- Land Surface Noah LSM (same as LIS)
- PBL MYJ scheme
- Cumulus parameterization None
8LIS Offline Spin-up Run
- LIS/Noah LSM run from 1 Jan 2004 to 1 Sep 2008
- Same soil and vegetation parameters as in WRF
- Atmospheric forcing
- 3-hourly Global Data Assimilation System analyses
- Hourly Stage IV radar gauge precipitation
products - Run long enough to allow soil to reach
equilibrium state - Output GRIB-1 files to initialize WRF land
surface variables - Incorporation of LIS data into WRF initial
condition - Slight modifications to WRF Preprocessing System
(WPS) - Created Vtable.LIS added LIS fields into
METGRID.TBL file - Soil moisture/temperature, skin temp, snow-water,
land-sea mask - LIS data over-write NAM land surface data
- Similarly, MODIS SSTs over-write NAM / RTG SSTs
in WPS
9Precip Verification with MET / MODE
- Traditional grid point verification
- Bias, threat score, HSS at various accumulation
intervals / thresholds - Neighborhood precipitation verification
- Occurrence of precipitation threshold in a box
surrounding a grid point - Relaxes stringency and determines model skill at
distance thresholds - MODE object classification
- Resolves objects through convolution
thresholding - Filter function applied to raw data using a
tunable radius of influence - Filtered field thresholded (tunable parameter) to
create mask field - Raw data restored to objects where mask
meets/exceeds threshold - For our study, MODE is run with
- 1-h, 3-h accumulated precipitation
- 5 mm, 10 mm, and 25 mm thresholds
- Radius of influence 12 km (produced best object
matching)
101 June 2008 Sensitivity Example0-10 cm Soil
Moisture Differences
111 June 2008 Sensitivity ExampleSST Differences
121 June 2008 Sensitivity ExampleWRF 3-h Precip
Diffs (06z to 06z)
Control
LIS
LIS CNTL
Stage IV
131 June 2008 Sensitivity ExampleMODE 5-mm / 3-h
precip Objects
Control Control LISMOD LISMOD
Fcst hour Grid Area Match Grid Area Un-match Grid Area Match Grid Area Un-match
3 0 1389 0 1389
6 0 169 0 206
9 0 1408 0 1421
12 0 2092 0 2066
15 54 1415 185 1351
18 3611 4415 3212 3864
21 5197 7602 6124 7034
24 1005 6362 1437 5868
27 1160 1013 102 2000
14Precip Verification Jun-Aug 2008
- WRF has high bias
- LISMOD reduces bias some, esp. during
daylight hours (12-24 h) - WRF generally has low skill (Heidke SS,
right) - LISMOD incrementally improves skill, esp. at
higher thresholds
15Neighborhood Precipitation Verification
16MODE Precip Object Verification
3-h Accumulated Precip Objects
1-h Accumulated Precip Objects
17Summary / Future Work
- Simulation methodology using NASA data and tools
- Land Information System land surface data
- MODIS SST composites
- Provides high-resolution representation of
land/water surface, consistent with local
regional modeling applications - Ongoing / Future efforts
- Conduct rigorous model verification
- Use MET to generate objective statistics and
object-oriented output for precipitation - Evaluate how combined NASA surface datasets can
lead to improved short-term local model forecasts
of convection - NASA / SPoRT website http//weather.msfc.nasa.gov
/sport/
18Backups
19LIS High-Level Overview
Coupled or Forecast Mode
Uncoupled or Analysis Mode
WRF
Station Data
Global, Regional Forecasts and (Re-) Analyses
ESMF
LSM Physics (e.g. Noah, VIC, SIB,SHEELS)
Satellite Products
20SPoRT MODIS SST Composites
- Real-time, 1-km SST product
- Composites available up to four times per day
- 0400, 0700, 1600, and 1900 UTC
- Primarily over Gulf of Mexico, western Atlantic
waters, and larger lakes (e.g. Floridas Lake
Okeechobee) - GRIB-1 files posted to publicly available ftp
site - Sub-sampled to 2-km spacing for model
applications - Compositing technique
- Build complete SST composite with multiple Earth
Observing System satellite passes (both Aqua and
Terra) - At each pixel, examine 5 most recent readings
- Take average of 3 warmest readings
- This method helps to eliminate cloud contamination
21Tropical Storm FayRainfall and Dramatic Soil
Moistening
22Tropical Storm FayRainfall and Dramatic Soil
Moistening